AI in Finance Summit New York schedule

•    Driving smarter decisions and innovation
•    Turning insights into business value
•    New skills for the AI era
•    Scaling AI responsibly in finance

 

 

•    From spreadsheets to smart agents: how finance teams are evolving their workflows.
•    Real-time insights at scale: using AI notebooks to make faster, revenue-driven decisions.
•    Reducing risk, boosting compliance: intelligent agents built for regulated environments.
•    Next-gen finance talent: empowering teams with tools that augment—not replace—their expertise.

•    Transitioning from static reports to continuous compliance
•    Empowering regulators and compliance officers with AI copilots
•    Automating AML/KYC through explainable AI

•    What are the most effective strategies to scale AI initiatives while keeping costs under control?
•    How can organizations balance the transformative potential of generative AI with the risks it introduces?
•    In what ways AI Agents are reshaping business operations, and what challenges come with their adoption?
•    How do you measure and ensure tangible business value from AI investments across the enterprise?

•    Using AI for secure, real-time global transactions
•    Overcoming fraud and data challenges in AI-powered payments 
•    The role of AI in CBDCs and next-gen payment rails 

 

•    How multi-agent systems are driving real-time decision-making in trading, risk, and compliance
•    The role of AI orchestration in creating autonomous financial ecosystems
•    Balancing innovation with human oversight and regulatory trust

•    Early signals of quantum computing integration with AI for portfolio optimization and fraud detection
•    Potential breakthroughs and realistic adoption timelines for finance
•    Managing hype vs. reality: what leaders should invest in today

•    Bridging the gap between data teams and business leadership
•    Enabling scalable AI through cross-functional collaboration
•    Using emotional intelligence to lead change and foster adoption
•    Positioning soft skills as a strategic advantage in AI-driven initiatives

•    Thriving in uncertainty, AI built to perform under market volatility and rapid change.
•    Trust by design, resilience rooted in transparency, governance, and ethical AI.
•    Future-ready finance, adaptable AI that meets new risks, regulations, and customer demands

•    Failures First – What early missteps revealed about AI’s real limits in finance.
•    Scaling Wins – How pioneers turned pilots into enterprise-wide impact.
•    Trust Factor – Building governance and transparency from the start.
•    Next Moves – What early adopters see as the boldest opportunities ahead.

•    Predicting systemic risks before they escalate
•    Partnering with central banks for resilient oversight
•    Designing human-centered, future-proof ecosystems

•    From Pilots to Scalable Impact: How financial institutions can evolve AI maturity to unlock measurable business outcomes.
•    The Growth Catalyst: Why AI maturity is the key to driving efficiency, innovation, and revenue generation in financial services.
•    Building Trust at Scale: How to align governance, compliance, and customer trust as AI capabilities mature.

•    How does poor data quality directly undermine AI outcomes in finance?
•    What governance practices truly build trust in AI-driven decisions?
•    How can institutions balance innovation with strict data controls?
•    What lessons from early adopters show the ROI of strong data governance?

•    Beyond automation: how AI agents move from task execution to strategic decision-making.
•    Efficiency unlocked: streamlining operations and reducing costs across the financial ecosystem.
•    Revenue engine: transforming AI agents into drivers of growth, new products, and customer value.
•    The future of finance: AI agents reshaping roles, talent, and business models.

 

 

•    How can financial institutions strike the right balance between rapid AI innovation and strict compliance requirements?
•    What are the most common security risks when deploying AI in finance, and how can organizations mitigate them?
•    How can firms ensure that AI tools are not only compliant at launch but remain compliant as regulations evolve?
•    What does it take to scale AI solutions across an enterprise without compromising governance or customer trust?
•    Looking ahead, what capabilities will define the next generation of AI tools that are truly enterprise-ready for finance

•    Empowering people, not replacing them: building AI teams that thrive in regulated financial environments.
•    Trust at every step: embedding human-in-the-loop strategies to ensure responsible governance.
•    Culture as the catalyst: driving organization-wide adoption of AI through mindset and leadership transformation.

Discover groundbreaking AI technologies aiming to transform finance

•    How can financial institutions ensure the quality, integrity, and governance of customer data to drive reliable AI outcomes?
•    In what ways does trusted customer data accelerate the development of innovative, personalized financial products and services?
•    What are the biggest challenges in balancing customer privacy with the need for data to fuel AI innovation?
•    How can organizations build and maintain customer trust while scaling AI solutions that rely heavily on sensitive data?

•    Collaborative Frameworks: Regulators and institutions shaping AI guardrails together.
•    Trust by Design: Embedding transparency, ethics, and accountability into every AI solution.
•    Compliance as Innovation: Leveraging regulation to unlock new opportunities and customer value.
•    Future-Proof Finance: Ensuring AI systems adapt to shifting rules, risks, and market dynamics

•    Domain Accuracy: Assess how LLMs perform on finance-specific reasoning tasks  
•    Safety & Compliance: Measure domain-specific safety, ethics, and regulatory alignment 
•    Cost vs Capability Trade-offs: Compare performance, inference cost, latency, and scalability in live financial use
•    Agentic & Tool-Enabled Behavior: Go beyond single-response metrics: evaluate reasoning chains, tool use, memory, and multi-step workflows

•    Clarity in Complexity: Why explainability is essential for financial institutions adopting AI at scale.
•    Trust & Compliance: Meeting regulatory requirements and strengthening customer confidence through transparent AI.
•    From Black Box to Glass Box: Practical approaches to make AI decisions understandable and auditable in finance.
•    Responsible Innovation: Balancing advanced AI capabilities with accountability, fairness, and ethical considerations



 

•    From Reactive to Proactive: AI-powered analytics to predict and prevent fraud before it happens.
•    Real-Time Defense: Stopping fraudulent transactions instantly while protecting customer experience.
•    Agile & Adaptive Models: Continuously evolving with new fraud schemes and financial crime tactics.
•    Trust as Currency: Strengthening compliance, transparency, and customer confidence through responsible AI.